class_docstr: Mean ensemble.
  
  Attributes:
    logits: Logits :class:`tf.Tensor` or dict of string to logits
      :class:`tf.Tensor` (for multi-head).
    subnetworks: List of :class:`adanet.subnetwork.Subnetwork` instances that
      form this ensemble.
    predictions: Optional dict mapping prediction keys to Tensors. MeanEnsembler
      can export mean_last_layer if the subnetworks have the last_layer of the
      same dimension.
class_docstr_tok: ["Mean","ensemble",".","Attributes",":","logits",":","Logits",":","class",":","`","tf.Tensor","`","or","dict","of","string","to","logits",":","class",":","`","tf.Tensor","`","(","for","multi-head",")",".","subnetworks",":","List","of",":","class",":","`","adanet.subnetwork.Subnetwork","`","instances","that","form","this","ensemble",".","predictions",":","Optional","dict","mapping","prediction","keys","to","Tensors",".","MeanEnsembler","can","export","mean_last_layer","if","the","subnetworks","have","the","last_layer","of","the","same","dimension","."]
code: def __new__(cls, logits, subnetworks=None, predictions=None):
      return super(MeanEnsemble, cls).__new__(
          cls,
          logits=logits,
          subnetworks=list(subnetworks or []),
          predictions=predictions,
      )
  
file_path: adanet/adanet/ensemble/mean.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import collections
  from adanet.ensemble.ensembler import Ensemble
  from adanet.ensemble.ensembler import Ensembler
  import tensorflow.compat.v2
label_desc: Use image node_id 1 for calling the MeanEnsemble obj's underlying member method code with example usage: obj.__new__(cls, logits, subnetworks, predictions) and returns: super